Experimenters have come to a realization that a design can perform very well in terms of a particular statistical characteristic and still perform poorly in terms of a rival characteristic. Due to this studies have narrowed down to the area of optimality criteria. Some of these criteria include the alphabetic optimality criteria and compound optimality criteria. Compound optimality criteria are those that combine two or more alphabetic optimality criteria in one particular design. In this paper two alphabetic optimality D-and T-criteria are combined to obtain DT-compound optimality criteria for the existing second order rotatable designs using Balanced Incomplete Block Designs. The purpose of this paper is to bring a balance between to statistical properties; parameter estimation and model discrimination. This will aid those researchers who are interested in more than two desired traits in one design. In this analysis, we note that the more homogenous the design is the more optimal it becomes.
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